Machine Learning Model Interpretability for Precision Medicine

By Gajendra J. Katuwal, Robert Chen. 2016

64 citations recorded by the source corpus.

Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we show that complex models such as random forest can be made interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality with 80% balanced accuracy and were also were able to interpret the relative effect of the features on prediction at individual level.

Subfields: Artificial Intelligence, Epidemiology, Model Architectures, Medical & Health AI

Citations in the corpus

• Deterministic Local Interpretable Model-Agnostic Explanations for Stable Explainability (2021, unknown): “Katuwal et al. [16] used LIME in precision medicine and discussed the importance of interpretablility to understand the contribution of important features in decision making.”